chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http, //www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+*/
+
+using System;
+using System.Collections.Generic;
+using QuantConnect.Algorithm;
+using QuantConnect.Algorithm.Framework.Alphas;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.DataLibrary.Tests
+{
+    /// <summary>
+    /// Example algorithm of using RiskParityPortfolioConstructionModel
+    /// </summary>
+    public class RiskParityPortfolioAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            SetStartDate(2021, 2, 21);
+            SetEndDate(2021, 3, 30);
+            SetCash(100000);
+            SetSecurityInitializer(security => security.SetMarketPrice(GetLastKnownPrice(security)));
+
+            AddEquity("SPY", Resolution.Daily);
+            AddEquity("AAPL", Resolution.Daily);
+
+            AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
+            SetPortfolioConstruction(new RiskParityPortfolioConstructionModel());
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 252;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 514;
+
+        /// <summary>
+        /// Final status of the algorithm
+        /// </summary>
+        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Orders", "31"},
+            {"Average Win", "0.01%"},
+            {"Average Loss", "-0.01%"},
+            {"Compounding Annual Return", "5.057%"},
+            {"Drawdown", "4.900%"},
+            {"Expectancy", "-0.273"},
+            {"Start Equity", "100000"},
+            {"End Equity", "100509.82"},
+            {"Net Profit", "0.510%"},
+            {"Sharpe Ratio", "0.265"},
+            {"Sortino Ratio", "0.371"},
+            {"Probabilistic Sharpe Ratio", "38.927%"},
+            {"Loss Rate", "58%"},
+            {"Win Rate", "42%"},
+            {"Profit-Loss Ratio", "0.75"},
+            {"Alpha", "-0.092"},
+            {"Beta", "1.22"},
+            {"Annual Standard Deviation", "0.2"},
+            {"Annual Variance", "0.04"},
+            {"Information Ratio", "-0.748"},
+            {"Tracking Error", "0.088"},
+            {"Treynor Ratio", "0.043"},
+            {"Total Fees", "$31.65"},
+            {"Estimated Strategy Capacity", "$1200000000.00"},
+            {"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
+            {"Portfolio Turnover", "3.08%"},
+            {"Drawdown Recovery", "14"},
+            {"OrderListHash", "6194b89f404d05e8ba437ce38f4bc4a4"}
+        };
+
+    }
+}